Download e-book for iPad: Biometric Authentication: ECCV 2004 International Workshop, by Jongsun Kim, Jongmoo Choi, Juneho Yi (auth.), Davide
By Jongsun Kim, Jongmoo Choi, Juneho Yi (auth.), Davide Maltoni, Anil K. Jain (eds.)
This publication constitutes the refereed lawsuits of the foreign Biometric Authentication Workshop, BioAW 2004, held in Prague, Czech Republic, in could 2004, as a part of ECCV 2004.
The 30 revised complete papers awarded have been conscientiously reviewed and chosen for presentation. The papers are prepared in topical sections on face acceptance, fingerprint acceptance, template safety and defense, different biometrics, and fusion and multimodal bioinformatics.
Read Online or Download Biometric Authentication: ECCV 2004 International Workshop, BioAW 2004, Prague, Czech Republic, May 15th, 2004. Proceedings PDF
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Extra resources for Biometric Authentication: ECCV 2004 International Workshop, BioAW 2004, Prague, Czech Republic, May 15th, 2004. Proceedings
Shashua and Riklin Raviv. T. The quotient image: Class based re-rendering and recognition with varying illuminations. PAMI, 23(2):129–139, 2001.  T. Sim, S. Baker, and M. Bsat. The cmu pose, illumination, and expression (pie) database of human faces. In AFGR, pages 46–51, 2002.  M. Tistarelli, A. Lagorio, and E. Grosso. Understanding iconic image-based face biometrics. Biometric Authentication, pages 19–29, 2002.  M. P. Pentland. Eigenfaces for recognition. CogNeuro, 3(1):71–96, 1991.
The number of training samples per subject, k, increases from 2 to 9. In each round, k images are randomly selected from the database for training and the remaining images of the same subject for testing. For each k, 20 tests were performed and these results were averaged. Table 1 shows the average recognition rates (%). e. c-1) as the final dimensions. Our methods NLDA, NKFDA show an encouraging performance. 2 FERET Database We have to test our method on more complex and challenging datasets such as the FERET database.
The negative samples (pseudo-impostors) were generated by taking the three original shots of all other clients ((200-1) clients x 3 shots = 1194 patterns). A model has been trained for each client. In table 1, we provide the results obtained by our boosting approach (AdaPix) using different number of classifiers (50, 100, 150, 200). We provide also results obtained by a state-of-the-art approach, namely Normalized Correlation (NC) , and results obtained using boosted classifiers based on seven Haar-like features  (AdaHaar7).
Biometric Authentication: ECCV 2004 International Workshop, BioAW 2004, Prague, Czech Republic, May 15th, 2004. Proceedings by Jongsun Kim, Jongmoo Choi, Juneho Yi (auth.), Davide Maltoni, Anil K. Jain (eds.)